A phantom system for assessing the effects of membrane lipids on water proton relaxation

Testing quantitative magnetization transfer models with membrane lipids

PURPOSE

Quantitative magnetization transfer (qMT) models aim to quantify the contributions of lipids and macromolecules to the MRI signal. Hence, a model system that relates qMT parameters and their molecular sources may improve the interpretation of the qMT parameters. Here we used membrane lipid phantoms as a meaningful tool to study qMT models. By controlling the fraction and type of membrane lipids, we could test the accuracy, reliability, and interpretability of different qMT models.

METHODS

We formulated liposomes with various lipid types and water-to-lipids fractions and measured their signals with spoiled gradient-echo MT. We fitted three known qMT models and estimated six parameters for every model. We tested the accuracy and reproducibility of the models and compared the dependency among the qMT parameters. We compared the samples' qMT parameters with their water-to-lipid fractions and with a simple MTnorm (= MTon/MToff) calculation.

RESULTS

We found that the three qMT models fit the membrane lipids signals well. We also found that the estimated qMT parameters are highly interdependent. Interestingly, the estimated qMT parameters are a function of the membrane lipid type and also highly related to the water-to-lipid fraction. Finally, we find that most of the lipid sample's information can be captured using the common and easy to estimate MTnorm analysis.

CONCLUSION

qMT parameters are sensitive to both the water-to-lipid fraction and to the lipid type. Estimating the water-to-lipid fraction can improve the characterization of membrane lipids' contributions to qMT parameters. Similar characterizations can be obtained using the MTnorm analysis.

Revealing the MRI-Contrast in Optically Cleared Brains

The current consensus holds that optically-cleared specimens are unsuitable for Magnetic Resonance Imaging (MRI); exhibiting absence of contrast. Prior studies combined MRI with tissue-clearing techniques relying on the latter's ability to eliminate lipids, thereby fostering the assumption that lipids constitute the primary source of ex vivo MRI-contrast. Nevertheless, these findings contradict an extensive body of literature that underscores the contribution of other features to contrast. Furthermore, it remains unknown whether non-delipidating clearing methods can produce MRI-compatible specimens or whether MRI-contrast can be re-established. These limitations hinder the development of multimodal MRI-light-microscopy (LM) imaging approaches. This study assesses the relation between MRI-contrast, and delipidation in optically-cleared whole brains following different tissue-clearing approaches. It is demonstrated that uDISCO and ECi-brains are MRI-compatible upon tissue rehydration, despite both methods' substantial delipidating-nature. It is also demonstrated that, whereas Scale-clearing preserves most lipids, Scale-cleared brain lack MRI-contrast. Furthermore, MRI-contrast is restored to lipid-free CLARITY-brains without introducing lipids. Our results thereby dissociate between the essentiality of lipids to MRI-contrast. A tight association is found between tissue expansion, hyperhydration and loss of MRI-contrast. These findings then enabled us to develop a multimodal MRI-LM-imaging approach, opening new avenues to bridge between the micro- and mesoscale for biomedical research and clinical applications.

Non-invasive assessment of normal and impaired iron homeostasis in the brain

Strict iron regulation is essential for normal brain function. The iron homeostasis, determined by the milieu of available iron compounds, is impaired in aging, neurodegenerative diseases and cancer. However, non-invasive assessment of different molecular iron environments implicating brain tissue's iron homeostasis remains a challenge. We present a magnetic resonance imaging (MRI) technology sensitive to the iron homeostasis of the living brain (the r1-r2* relaxivity). In vitro, our MRI approach reveals the distinct paramagnetic properties of ferritin, transferrin and ferrous iron ions. In the in vivo human brain, we validate our approach against ex vivo iron compounds quantification and gene expression. Our approach varies with the iron mobilization capacity across brain regions and in aging. It reveals brain tumors' iron homeostasis, and enhances the distinction between tumor tissue and non-pathological tissue without contrast agents. Therefore, our approach may allow for non-invasive research and diagnosis of iron homeostasis in living human brains.

Macromolecular tissue volume mapping of lateral geniculate nucleus subdivisions in living human brains

The lateral geniculate nucleus (LGN) is a key thalamic nucleus in the visual system, which has an important function in relaying retinal visual input to the visual cortex. The human LGN is composed mainly of magnocellular (M) and parvocellular (P) subdivisions, each of which has different stimulus selectivity in neural response properties. Previous studies have discussed the potential relationship between LGN subdivisions and visual disorders based on psychophysical data on specific types of visual stimuli. However, these relationships remain speculative because non-invasive measurements of these subdivisions are difficult due to the small size of the LGN. Here we propose a method to identify these subdivisions by combining two structural MR measures: high-resolution proton-density weighted images and macromolecular tissue volume (MTV) maps. We defined the M and P subdivisions based on MTV fraction data and tested the validity of the definition by (1) comparing the data with that from human histological studies, (2) comparing the data with functional magnetic resonance imaging measurements on stimulus selectivity, and (3) analyzing the test-retest reliability. The findings demonstrated that the spatial organization of the M and P subdivisions was consistent across subjects and in line with LGN subdivisions observed in human histological data. Moreover, the difference in stimulus selectivity between the subdivisions identified using MTV was consistent with previous physiology literature. The definition of the subdivisions based on MTV was shown to be robust over measurements taken on different days. These results suggest that MTV mapping is a promising approach for evaluating the tissue properties of LGN subdivisions in living humans. This method potentially will enable neuroscientific and clinical hypotheses about the human LGN subdivisions to be tested.

A validation framework for neuroimaging software: The case of population receptive fields

Neuroimaging software methods are complex, making it a near certainty that some implementations will contain errors. Modern computational techniques (i.e., public code and data repositories, continuous integration, containerization) enable the reproducibility of the analyses and reduce coding errors, but they do not guarantee the scientific validity of the results. It is difficult, nay impossible, for researchers to check the accuracy of software by reading the source code; ground truth test datasets are needed. Computational reproducibility means providing software so that for the same input anyone obtains the same result, right or wrong. Computational validity means obtaining the right result for the ground-truth test data. We describe a framework for validating and sharing software implementations, and we illustrate its usage with an example application: population receptive field (pRF) methods for functional MRI data. The framework is composed of three main components implemented with containerization methods to guarantee computational reproducibility. In our example pRF application, those components are: (1) synthesis of fMRI time series from ground-truth pRF parameters, (2) implementation of four public pRF analysis tools and standardization of inputs and outputs, and (3) report creation to compare the results with the ground truth parameters. The framework was useful in identifying realistic conditions that lead to imperfect parameter recovery in all four pRF implementations, that would remain undetected using classic validation methods. We provide means to mitigate these problems in future experiments. A computational validation framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. We hope that the framework will be helpful to validate other critical neuroimaging algorithms, as having a validation framework helps (1) developers to build new software, (2) research scientists to verify the software's accuracy, and (3) reviewers to evaluate the methods used in publications and grants.